This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction.
Estadística bayesianaUniversidad Duke
Acerca de este Curso
Habilidades que obtendrás
- 5 stars45,09 %
- 4 stars20,63 %
- 3 stars14,52 %
- 2 stars9,17 %
- 1 star10,57 %
Principales reseñas sobre ESTADÍSTICA BAYESIANA
I wanted to tools for Bayesian Statistics to be as functional as the other tools available. No problem with the class. I think the material will get there for R.
I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.
The section about Beta-Binomial Conjugate is taught very fast and unless the student is quite familiar with Beta and Gamma distributions, it makes it very difficult to follow the course.
This is the hardest courses I have taken. I hoped to have more supplemental reading materials and more practical exercises in R.
¿Cuándo podré acceder a las lecciones y tareas?
¿Qué recibiré si me suscribo a este Programa especializado?
¿Hay ayuda económica disponible?
What background knowledge is necessary?
Will I receive a transcript from Duke University for completing this course?
¿Tienes más preguntas? Visita el Centro de Ayuda al Estudiante.